Neural recommender models [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]} have been actively studied for representing complex and non-linear factors across user-item interactions. Although deep neural networks (DNNs) are generally capable of identifying the complex patterns of user-item interactions, recent studies [18]}, [19]} reported that the performance gain in the recommendation problem is less path-breaking than in other domains such as computer vision, speech recognition, or natural language processing. There may be multiple reasons, such as extreme data sparsity, data ambiguity for missing feedback, and noisy user feedback. However, it is still an open question as to why DNNs have achieved less satisfactory improvements in the recommendation problem.
